Evidence map›Paper›PMID 39661968›Full record

SynthesisJournal of medical Internet research2024

Large Language Models and Empathy: Systematic Review.

Vera Sorin, Dana Brin, Yiftach Barash, Eli Konen, Alexander Charney, Girish Nadkarni, Eyal Klang

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

81 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Article
  4. Empathy Cuts Both Ways in Clinical AI.JMIR human factors · 2026
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  6. What Are Physicians For? How Psychiatry's Competencies Endure Into the Age of AI.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
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21 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Vera SorinDepartment of Radiology, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0003-0509-4686
Dana BrinDepartment of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel.ORCID 0009-0003-7316-206X
Yiftach BarashDepartment of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel.ORCID 0000-0002-7242-1328
Eli KonenDepartment of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel.ORCID 0000-0001-9507-2450
Alexander CharneyDivision of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.ORCID 0000-0001-8135-6858
Girish NadkarniDivision of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.ORCID 0000-0001-6319-4314
Eyal KlangDivision of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.ORCID 0000-0002-4567-3108

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmpathy, a fundamental aspect of human interaction, is characterized as the ability to experience another being's emotions within oneself. In health care, empathy is a fundamental for health care professionals and patients' interaction. It is a unique quality to humans that large language models (LLMs) are believed to lack.

objectiveWe aimed to review the literature on the capacity of LLMs in demonstrating empathy.

methodsWe conducted a literature search on MEDLINE, Google Scholar, PsyArXiv, medRxiv, and arXiv between December 2022 and February 2024. We included English-language full-length publications that evaluated empathy in LLMs' outputs. We excluded papers evaluating other topics related to emotional intelligence that were not specifically empathy. The included studies' results, including the LLMs used, performance in empathy tasks, and limitations of the models, along with studies' metadata were summarized.

resultsA total of 12 studies published in 2023 met the inclusion criteria. ChatGPT-3.5 (OpenAI) was evaluated in all studies, with 6 studies comparing it with other LLMs such GPT-4, LLaMA (Meta), and fine-tuned chatbots. Seven studies focused on empathy within a medical context. The studies reported LLMs to exhibit elements of empathy, including emotions recognition and emotional support in diverse contexts. Evaluation metric included automatic metrics such as Recall-Oriented Understudy for Gisting Evaluation and Bilingual Evaluation Understudy, and human subjective evaluation. Some studies compared performance on empathy with humans, while others compared between different models. In some cases, LLMs were observed to outperform humans in empathy-related tasks. For example, ChatGPT-3.5 was evaluated for its responses to patients' questions from social media, where ChatGPT's responses were preferred over those of humans in 78.6% of cases. Other studies used subjective readers' assigned scores. One study reported a mean empathy score of 1.84-1.9 (scale 0-2) for their fine-tuned LLM, while a different study evaluating ChatGPT-based chatbots reported a mean human rating of 3.43 out of 4 for empathetic responses. Other evaluations were based on the level of the emotional awareness scale, which was reported to be higher for ChatGPT-3.5 than for humans. Another study evaluated ChatGPT and GPT-4 on soft-skills questions in the United States Medical Licensing Examination, where GPT-4 answered 90% of questions correctly. Limitations were noted, including repetitive use of empathic phrases, difficulty following initial instructions, overly lengthy responses, sensitivity to prompts, and overall subjective evaluation metrics influenced by the evaluator's background.

conclusionsLLMs exhibit elements of cognitive empathy, recognizing emotions and providing emotionally supportive responses in various contexts. Since social skills are an integral part of intelligence, these advancements bring LLMs closer to human-like interactions and expand their potential use in applications requiring emotional intelligence. However, there remains room for improvement in both the performance of these models and the evaluation strategies used for assessing soft skills.

Indexed as

EmpathyHumansLanguageAIChatGPTcognitiveemotional intelligenceemotionsempathyfoundation modelshealthcarehuman assessmenthuman interactionLLMsobjective metricsPRISMAreview methodologyreview methodsscopingsynthesissystematic reviewtext-based

Identifiers

PMID39661968
PMCPMC11669866

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.